code: def _maybe_extend_df(
      df: pd.DataFrame,
      n_forecasts: int,
      max_lags: int,
      freq: Optional[str],
      config_regressors: Optional[OrderedDictType[str, Regressor]],
      config_events: Optional[ConfigEvents],
  ) -> Tuple[pd.DataFrame, dict]:
      """
      Extend the input DataFrame based on the number of forecasts, maximum lags,
      frequency, regressor configuration, and event configuration.
  
      Parameters
      ----------
      df : pd.DataFrame
          Input DataFrame to be extended.
      n_forecasts : int
          Number of forecasts to be made.
      max_lags : int
          Number of steps ahead of prediction time step to forecast.
      freq : str
          Frequency of the time series data.
      config_regressors : OrderedDict[str, Regressor]
          Configuration of regressors.
      config_events : ConfigEvents
          Configuration of events.
  
      Returns
      -------
      tuple[pd.DataFrame, int]
          A tuple containing the extended DataFrame and the periods added.
      """
      # Receives df with ID column
      periods_add = {}
      extended_df = pd.DataFrame()
      for df_name, df_i in df.groupby("ID"):
          _ = df_utils.infer_frequency(df_i, n_lags=max_lags, freq=freq)
          # to get all forecasteable values with df given, maybe extend into future:
          periods_add[df_name] = _get_maybe_extend_periods(
              df=df_i,
              n_forecasts=n_forecasts,
              max_lags=max_lags,
              config_regressors=config_regressors,
          )
          if periods_add[df_name] > 0:
              # This does not include future regressors or events.
              # periods should be 0 if those are configured.
              last_date = (
                  pd.to_datetime(df_i["ds"].copy(deep=True))
                  .sort_values()
                  .max()
              )
              future_df = df_utils.make_future_df(
                  df_columns=df_i.columns,
                  last_date=last_date,
                  periods=periods_add[df_name],
                  freq=freq,
                  config_events=config_events,
              )
              future_df["ID"] = df_name
              df_i = pd.concat([df_i, future_df])
              df_i.reset_index(drop=True, inplace=True)
          extended_df = pd.concat(
              (extended_df, df_i.copy(deep=True)), ignore_index=True
          )
      return extended_df, periods_add
  
file_path: neural_prophet/neuralprophet/data/split.py
imports: import logging
  from typing import Optional
  from typing import OrderedDict
  from typing import Tuple
  import pandas
  from neuralprophet import df_utils
  from neuralprophet.configure import ConfigEvents, Regressor
  from neuralprophet.data.process import _check_dataframe
label_desc: Use image node_id 1 for calling a global function with example usage: _maybe_extend_df(df, n_forecasts, max_lags, freq, config_regressors, config_events) and returns: extended_df,  periods_add